• Environmental and Resource Economics Review
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• v.21 no.1
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• pp.27-67
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• 2012

This paper analyzed the effect of transaction costs on the prices and trading volumes at the initial stage of emission markets and also examined how the size of the effect differs depending on the characteristics of the transactions. We built trading protocols modeling a recursive process to search the trading partner and make transactions with several behavioral assumptions considering the situations of early markets. The simulations results show that adding transaction costs resulted in reduction of trading volumes. Furthermore, the speed of reduction in trading volume to the increase of transaction costs is higher when there is scale economy. With a certain level of scale economy, the trading volumes abruptly fall down to almost zero as the transaction cost gets over a certain level. This suggests the possibility of a failed market. Since the scale economy is thought to be significant in the early stage of emission trading market, it is desirable to design a trading system that maximizes trading volumes and minimizes unit transaction costs at the outset. One of the alternatives to meet these conditions is to establish a centralized exchange and take measures to increase trading volumes.

• IE interfaces
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• v.24 no.4
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• pp.379-386
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• 2011

The importance of renewable energy technology is discussed and next generation power transmission networks, which is called the smart grid, are constructed in developed countries. However for construction and operation of the smart grid, it is required not only to develop the electrical power generation technologies and transmission equipments but also to study systematic analysis and optimization for design and operation of the smart grid. In this paper we study electrical power trade in the smart grid using operations research models and simulation methods. We also consider future electrical power exchange markets in Korea and build four scenarios and the related optimization and simulation models, which reflect electrical power transaction pricing strategies of stake-holders. We can also simulate electrical power exchange market and analyze the results of electrical power trading, which can give us some insights for future electrical power exchange market.

• Environmental and Resource Economics Review
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• v.13 no.2
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• pp.271-300
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• 2004

We investigate the two recent simulations of the proto-type domestic carbon emission trading system in Korea and draw some policy implications. The first simulation includes the 5 electric power companies based on baseline and credit. But the second one is with the 7 energy-intensive companies based on cap and trade. The voluntary approaches in this paper revealed the instability of market equilibrium, i.e., price volatility or distortion, excess supply or demand. These phenomena stems from excess incentives to the players, asymmetric information, players' irresponsible strategic behaviors, and non acquaintance of trading system. This paper suggests the basic design for domestic carbon trading system in future and a stepwise introduction strategy for it including the incentive auction scheme, the total quantity of incentive needed, and how to finance it. Meantime, the further simulations on the various sectors based on voluntary participation must be essential for learning experiences and better policy design.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.187-201
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• 2011

KOSPI200 index is the Korean stock price index consisting of actively traded 200 stocks in the Korean stock market. Its base value of 100 was set on January 3, 1990. The Korea Exchange (KRX) developed derivatives markets on the KOSPI200 index. KOSPI200 index futures market, introduced in 1996, has become one of the most actively traded indexes markets in the world. Traders can make profit by entering a long position on the KOSPI200 index futures contract if the KOSPI200 index will rise in the future. Likewise, they can make profit by entering a short position if the KOSPI200 index will decline in the future. Basically, KOSPI200 index futures trading is a short-term zero-sum game and therefore most futures traders are using technical indicators. Advanced traders make stable profits by using system trading technique, also known as algorithm trading. Algorithm trading uses computer programs for receiving real-time stock market data, analyzing stock price movements with various technical indicators and automatically entering trading orders such as timing, price or quantity of the order without any human intervention. Recent studies have shown the usefulness of artificial intelligent systems in forecasting stock prices or investment risk. KOSPI200 index data is numerical time-series data which is a sequence of data points measured at successive uniform time intervals such as minute, day, week or month. KOSPI200 index futures traders use technical analysis to find out some patterns on the time-series chart. Although there are many technical indicators, their results indicate the market states among bull, bear and flat. Most strategies based on technical analysis are divided into trend following strategy and non-trend following strategy. Both strategies decide the market states based on the patterns of the KOSPI200 index time-series data. This goes well with Markov model (MM). Everybody knows that the next price is upper or lower than the last price or similar to the last price, and knows that the next price is influenced by the last price. However, nobody knows the exact status of the next price whether it goes up or down or flat. So, hidden Markov model (HMM) is better fitted than MM. HMM is divided into discrete HMM (DHMM) and continuous HMM (CHMM). The only difference between DHMM and CHMM is in their representation of state probabilities. DHMM uses discrete probability density function and CHMM uses continuous probability density function such as Gaussian Mixture Model. KOSPI200 index values are real number and these follow a continuous probability density function, so CHMM is proper than DHMM for the KOSPI200 index. In this paper, we present an artificial intelligent trading system based on CHMM for the KOSPI200 index futures system traders. Traders have experienced on technical trading for the KOSPI200 index futures market ever since the introduction of the KOSPI200 index futures market. They have applied many strategies to make profit in trading the KOSPI200 index futures. Some strategies are based on technical indicators such as moving averages or stochastics, and others are based on candlestick patterns such as three outside up, three outside down, harami or doji star. We show a trading system of moving average cross strategy based on CHMM, and we compare it to a traditional algorithmic trading system. We set the parameter values of moving averages at common values used by market practitioners. Empirical results are presented to compare the simulation performance with the traditional algorithmic trading system using long-term daily KOSPI200 index data of more than 20 years. Our suggested trading system shows higher trading performance than naive system trading.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.931-934
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• 2005

This paper describes an adaptive recommendation system that provides real-time personalized trading advice to the investors based on their profiles and trading information environment. A proposed system integrates Stochastic technical analysis and artificial neural network that incorporates an adaptive user modeling. The user model is constructed and updated based on initial user profile and recorded user interactions with the system. The information presented to each individual user is also tailor-made to fit the user's behavior and preference. A system prototype was implemented in JAVA. Experiments used to evaluate the system's performance were done on both human subjects and synthetic users. The results show our proposed system is able to rapidly learn to provide appropriate advice to different types of users.

• Journal of information and communication convergence engineering
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• v.11 no.3
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• pp.190-198
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• 2013

Recently, one of the most vital advancement in the field of finance is high-performance trading using field-programmable gate array (FPGA). The objective of this paper is to design high-performance Black Scholes option trading system on an FPGA. We implemented an efficient Black Scholes Call Option System IP on an FPGA. The IP may perform 180 million transactions per second after initial latency of 208 clock cycles. The implementation requires the 64-bit IEEE double-precision floatingpoint adder, multiplier, exponent, logarithm, division, and square root IPs. Our experimental results show that the design is highly efficient in terms of frequency and resource utilization, with the maximum frequency of 179 MHz on Altera Stratix V.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.11a
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• pp.489-497
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• 2000

After IMF situation, the money market environment is changing rapidly. Therefore, many companies including financial institutions and many individual investors are concerned about forecasting the money market, and they make an effort to insure the various profit and hedge methods using derivatives like option, futures and swap. In this research, we developed a prototype of forecasting system for KOSPI 200 option, especially call option, trading using artificial neural networks(ANN), To avoid the overfitting problem and the problem involved int the choice of ANN structure and parameters, we employed the ANN ensemble approach. We conducted two types of simulation. One is conducted with the hold signals taken into account, and the other is conducted without hold signals. Even though our models show low accuracy for the sample set extracted from the data collected in the early stage of IMF situation, they perform better in terms of profit and stability than the model that uses only the theoretical price.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.6
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• pp.163-171
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• 2021

In this paper, the energy transaction system was optimized by applying a resource allocation algorithm and deep reinforcement learning in the distributed power system. The power demand and supply environment were predicted by deep reinforcement learning. We propose a system that pursues common interests in power trading and increases the efficiency of long-term power transactions in the paradigm shift from conventional centralized to distributed power systems in the power trading system. For a realistic energy simulation model and environment, we construct the energy market by learning weather and monthly patterns adding Gaussian noise. In simulation results, we confirm that the proposed power trading systems are cooperative with each other, seek common interests, and increase profits in the prolonged energy transaction.

• International Area Studies Review
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• v.16 no.3
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• pp.239-266
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• 2012

The purpose of this paper is to suggest desirable improvement for KRX derivatives market plagued with many problems in spite of its world level of quantitative growth. In order to try to find desirable improvement for KRX derivatives market which has many problems like that, I suggest various ways of improvement for regulatory system in the future in terms of behavioral regulation for investor protection. First of all, in order to relieve speculative tendency of trading, KOSPI200 option market with ATM-oriented option trading needs to be induced from the market in which OTM-oriented option is now trading. So discount or exemption of brokerage fee for ATM trading and the introduction of market-maker for ATM type can be considered. For the protection of individual investors, we suggest feasible plans such as differential regulation between professional and individual investors, consolidation of basic deposit management, and enlargement of opportunities for risk management education & simulation trading.

• Environmental and Resource Economics Review
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• v.13 no.2
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• pp.301-322
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• 2004

The current petroleum stockpiling by Korean government is based on the static concept of dead-stock. However, the recent changes in economic environment is requiring a transition to the dynamic concept of flow-stock. This study suggested selective trading strategies using basis of changing oil prices as an option for improving the economics of domestic strategic petroleum reserve (SPR), and quantitatively analyzed their effects. For this purpose, we tested the validity of selective trading strategies using the weekly spot and forwards prices of WTI for the period of October 1997 to August 2002. Summarizing the simulation results, the selective trading strategies would increase the expected values of profits and decrease their volatilities compared to those of traditional routine strategies. And, the adoption of trigger value could increase the improvements by the selective trading strategies. Based on the results, we suggest that, in order to improve the economics of domestic petroleum stockpiling, it is necessary to actively utilize the reserve facilities and the reserved petroleum with proper derivatives position.